Conventional computer systems including robots are not able to improve their efficiency automatically by substantially changing and extending the programs or the knowledge in their memories (Feigenbaum, E. A., und McCorduck, P., The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Addison-Wesley, Menlo Park, Calif., 1983, p. 237). Such changes and extensions of the programs or the knowledge in machines are conventionally executed by human experts on the basis of an analysis of a field of application, that is, the programs or the knowledge are manually developed on the basis of a manual analysis of the field of application.
The ordinary use of conventional computer systems is that programs, which are compositions of elementary complete instructions, and data, which are to be processed by the programs, are loaded into their memories by means of input units, that the programs are applied to the data by means of processors, and that the final results are transmitted to output units.
The field of expert systems relates to development of computer systems having special knowledge in a field of application (Hayes-Roth, F., Waterman, D. A., and Lenat, D. B., Building Expert Systems, Addison-Wesley, London, 1983). An expert system consists of a knowledge base and an inference machine. The knowledge base contains knowledge in a field of application and the inference machine methods for applying this knowledge. The knowledge consists of facts in the field of application and heuristic rules containing experiental knowledge. The knowledge base and the inference machine are manually developed on the basis of a manual analysis of the field of application. In tightly restricted fields of application the efficiency of expert systems is comparable to that of human experts (ibid., p. 38). Two well-known expert systems are MYCIN and R1. MYCIN diagnoses blood deseases and offers advice for their therapy (Shortliffle, E. H., MYCIN: Computer-Based Medical Consultations, American Elsevier, New York, 1976). R1 generates configurations for the VAX computer system of Digital Equipment Corporation on the basis of customer orders. These configurations consist of diagrams which contain the spacial relations of the components in the orders (McDermott, J., "R1: A Rule-Based Configurer of Computer Systems", Technical Report CMU-CS-80-119, Computer Science Department, Carnegie-Mellon University, Pittsburg, Pa. 1980).
The field of machine learning has the object to develop computer systems that change and extend the knowledge in their memories. Some well-known learning systems are briefly described subsequently. Systems using a method called explanation-based learning transform a manually given inefficient definition of a concept to be learned into an efficient definition on the basis of a manually given description of an example, a manually given theory of a field of application, and manually given criteria specifying efficient expressions in which the concept to be learned shall be represented (Mitchell, T. M., Keller, R. M., and Cedar-Cabelli, S. T., "Explanation-based generalization: A unifying view", Machine Learning, Vol. 1, No. 1, 1986, pp. 47-80). The LP system learns new methods for solving equations by a procedure called precondition analysis (Silver, B., "Precondition analysis: Learning control information", in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Hrsg.), Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, Los Altos, Calif., 1986). LP uses a manually given sophisticated syntax for his methods and begins with some fifteen methods (ibid., p. 651). The AM system discovered mathematical definitions and conjectures (Lenat, D. B., "AM: Discovery in mathematics as heuristic search", in D. B. Lenat und R. Davis, Knowledge-Based Systems in Artificial Intelligence, McGraw-Hill, New York, 1982). It is based on a set of manually given concepts, a set of manually given heuristic rules, and a detailed model of mathematical research developed by human experts (ibid., pp. 61-101, pp. 152-161, pp. 35-59, pp. 163-204, and pp. 147-149). The EURISKO system, which is a further development of AM, was also applied to fields outside of mathematics (Lenat, D. B., "EURISKO: A program that learns new heuristics and domain concepts", Artificial Intelligence, Vol. 21, 1983, pp. 61-98). It is also based on a set of manually given concepts, a set of manually given heuristic rules, and detailed models of the fields of application which were developed by human experts. Thus, learning systems are based on sophisticated manually developed methods and extensive complex knowledge which are also manually developed on the basis of a manual analysis of the fields of application. The complexity of the fields of application of conventional learning systems corresponds to the complexity of problems in high-school mathematics or the complexity of discoveries in elementary mathematics (Mitchell, T. M., Keller, R. M., and Cedar-Cabelli, S. T., "Explanation-based generalization: A unifying view", Machine Learning, Vol. 1, No. 1, 1986, pp. 61-64; Silver, B., "Precondition analysis: Learning control information", in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, Los Altos, Calif., 1986, p. 647; Lenat, D. B., "AM: Discovery in mathematics as heuristic search", in D. B. Lenat und R. Davis, Knowledge-Based Systems in Artificial Intelligence, McGraw-Hill, New York, 1982, p. 7).
Outside of the field of machine learning, there were no successful attempts to develop theories, in particular, methods that can be executed by machines, for the construction of new knowledge (Simon, H. A., "Does scientific discovery have a logic?", Philosophy of Science, Vol. 40, No. 4, 1973, p. 474; Simon, H. A., and Newell, A., "Informationsverarbeitung und Problemosen", in G. Steiner (ed.), Piaget und die Folgen, Kindler, Zurich, Switzerland, 1978, p. 247). The object of these unsuccessful attempts was to develop such theories or methods manually. Well-known scientists such as A. Einstein, W. Heisenberg, and K. R. Popper supposed that there are no theories concerning the development of new knowledge, in particular, no mechanical methods for its construction (see Ammon, K., "The Automatic Development of Concepts and Methods", Doctoral Dissertation, University of Hamburg, 1988, p. 178).
The above overview of conventional computer systems, expert systems, learning systems, and other systems concerning the construction of new knowledge shows that these systems are based on manually developed theories which contain the programs, the methods, or the knowledge in these systems. These theories are developed on the basis of a manual analysis of a field of application. This means that there are non-elementary explicit theories of the processes that process information or generate new knowledge in these systems. This entails that the information in the inputs and the outputs these systems process, in particular, the relations between this information in the inputs and outputs, can completely be covered by explicit theories. Therefore, these systems have the disadvantages that they cannot by applied in complex fields that cannot completely be covered by given explicit theories and thus automatic changes and extensions of their theories, that is, their programs, their methods, and their knowledge, are required. For example, computer programs are developed by human experts in costly working processes. Therefore, conventional computer systems cannot be applied in fields that cannot completely be covered by explicit theories. Conventional expert systems and conventional learning systems also have these disadvantages: The extension of expert systems beyond the field of application originally contemplated by their designers is very difficult (McCarthy, J., "Some expert systems need common sense", Annals of the New York Academy of Sciences, Vol. 426, 1983, pp. 129-137) and the efficiency of learning systems decreases drastically after some period of time (Lenat, D. B., "AM: Discovery in mathematics as heuristic search", in D. B. Lenat und R. Davis, Knowledge-Based Systems in Artificial Intelligence, McGraw-Hill, New York, 1982, p. 7 und p. 135; Mitchell, T. M., "Learning and problem solving", Proceedings of the Eighth International Conference on Artificial Intelligence, Karlsruhe, West Germany, August 1983, Kaufmann, Los Altos, Calif.).